from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


lowerCamelCase__  =					logging.get_logger(__name__)




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''pixel_values''']







 def __init__(		self				:   List[str]							, lowercase_				:   bool = True							, lowercase_				:   Dict[str, int] = None							, lowercase_				:   PILImageResampling = PIL.Image.BICUBIC							, lowercase_				:   bool = True							, lowercase_				:   Dict[str, int] = None							, lowercase_				:   Union[int, float] = 1 / 255							, lowercase_				:   bool = True							, lowercase_				:   bool = True							, lowercase_				:   Optional[Union[float, List[float]]] = None							, lowercase_				:   Optional[Union[float, List[float]]] = None							, **lowercase_				:   List[str]							, )						->	None:


   """simple docstring"""







   super().__init__(**lowercase_)
   _UpperCamelCase           =    size if size is not None else {"height": 256, "width": 256}
   _UpperCamelCase           =    get_size_dict(lowercase_)
   _UpperCamelCase           =    crop_size if crop_size is not None else {"height": 224, "width": 224}
   _UpperCamelCase           =    get_size_dict(lowercase_							, param_name="crop_size")

   _UpperCamelCase           =    do_resize
   _UpperCamelCase           =    size
   _UpperCamelCase           =    resample
   _UpperCamelCase           =    do_center_crop
   _UpperCamelCase           =    crop_size
   _UpperCamelCase           =    do_rescale
   _UpperCamelCase           =    rescale_factor
   _UpperCamelCase           =    do_normalize
   _UpperCamelCase           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   _UpperCamelCase           =    image_std if image_std is not None else IMAGENET_STANDARD_STD







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   np.ndarray							, lowercase_				:   Dict[str, int]							, lowercase_				:   PILImageResampling = PIL.Image.BICUBIC							, lowercase_				:   Optional[Union[str, ChannelDimension]] = None							, **lowercase_				:   Tuple							, )						->	np.ndarray:


   """simple docstring"""







   _UpperCamelCase           =    get_size_dict(lowercase_)
   if "height" not in size or "width" not in size:
     raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
   return resize(
       lowercase_							, size=(size["height"], size["width"])							, resample=lowercase_							, data_format=lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   np.ndarray							, lowercase_				:   Dict[str, int]							, lowercase_				:   Optional[Union[str, ChannelDimension]] = None							, **lowercase_				:   int							, )						->	np.ndarray:


   """simple docstring"""







   _UpperCamelCase           =    get_size_dict(lowercase_)
   if "height" not in size or "width" not in size:
     raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
   return center_crop(lowercase_							, size=(size["height"], size["width"])							, data_format=lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   np.ndarray							, lowercase_				:   Union[int, float]							, lowercase_				:   Optional[Union[str, ChannelDimension]] = None							, **lowercase_				:   str							, )						->	Union[str, Any]:


   """simple docstring"""







   return rescale(lowercase_							, scale=lowercase_							, data_format=lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   np.ndarray							, lowercase_				:   Union[float, List[float]]							, lowercase_				:   Union[float, List[float]]							, lowercase_				:   Optional[Union[str, ChannelDimension]] = None							, **lowercase_				:   Union[str, Any]							, )						->	np.ndarray:


   """simple docstring"""







   return normalize(lowercase_							, mean=lowercase_							, std=lowercase_							, data_format=lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   ImageInput							, lowercase_				:   bool = None							, lowercase_				:   Dict[str, int] = None							, lowercase_				:   Any=None							, lowercase_				:   bool = None							, lowercase_				:   Dict[str, int] = None							, lowercase_				:   bool = None							, lowercase_				:   float = None							, lowercase_				:   bool = None							, lowercase_				:   Optional[Union[float, List[float]]] = None							, lowercase_				:   Optional[Union[float, List[float]]] = None							, lowercase_				:   Optional[Union[str, TensorType]] = None							, lowercase_				:   ChannelDimension = ChannelDimension.FIRST							, **lowercase_				:   Optional[Any]							, )						->	PIL.Image.Image:


   """simple docstring"""







   _UpperCamelCase           =    do_resize if do_resize is not None else self.do_resize
   _UpperCamelCase           =    resample if resample is not None else self.resample
   _UpperCamelCase           =    do_center_crop if do_center_crop is not None else self.do_center_crop
   _UpperCamelCase           =    do_rescale if do_rescale is not None else self.do_rescale
   _UpperCamelCase           =    rescale_factor if rescale_factor is not None else self.rescale_factor
   _UpperCamelCase           =    do_normalize if do_normalize is not None else self.do_normalize
   _UpperCamelCase           =    image_mean if image_mean is not None else self.image_mean
   _UpperCamelCase           =    image_std if image_std is not None else self.image_std

   _UpperCamelCase           =    size if size is not None else self.size
   _UpperCamelCase           =    get_size_dict(lowercase_)
   _UpperCamelCase           =    crop_size if crop_size is not None else self.crop_size
   _UpperCamelCase           =    get_size_dict(lowercase_							, param_name="crop_size")

   _UpperCamelCase           =    make_list_of_images(lowercase_)

   if not valid_images(lowercase_):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray.")

   if do_resize and size is None or resample is None:
     raise ValueError("Size and resample must be specified if do_resize is True.")

   if do_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True.")

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True.")

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("Image mean and std must be specified if do_normalize is True.")

   # All transformations expect numpy arrays.
   _UpperCamelCase           =    [to_numpy_array(lowercase_) for image in images]

   if do_resize:
     _UpperCamelCase           =    [self.resize(image=lowercase_							, size=lowercase_							, resample=lowercase_) for image in images]

   if do_center_crop:
     _UpperCamelCase           =    [self.center_crop(image=lowercase_							, size=lowercase_) for image in images]

   if do_rescale:
     _UpperCamelCase           =    [self.rescale(image=lowercase_							, scale=lowercase_) for image in images]

   if do_normalize:
     _UpperCamelCase           =    [self.normalize(image=lowercase_							, mean=lowercase_							, std=lowercase_) for image in images]

   _UpperCamelCase           =    [to_channel_dimension_format(lowercase_							, lowercase_) for image in images]

   _UpperCamelCase           =    {"pixel_values": images}
   return BatchFeature(data=lowercase_							, tensor_type=lowercase_)





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ = None    ,     a__ = None    ,     a__ = False    ,     )      ->tuple[int, float, str]:




  '''simple docstring'''

  _UpperCamelCase           =    cipher_alphabet or [chr(a__ ) for i in range(97    ,     123 )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    _UpperCamelCase           =    {
        "a": 0.08497,
        "b": 0.01492,
        "c": 0.02202,
        "d": 0.04253,
        "e": 0.11162,
        "f": 0.02228,
        "g": 0.02015,
        "h": 0.06094,
        "i": 0.07546,
        "j": 0.00153,
        "k": 0.01292,
        "l": 0.04025,
        "m": 0.02406,
        "n": 0.06749,
        "o": 0.07507,
        "p": 0.01929,
        "q": 0.00095,
        "r": 0.07587,
        "s": 0.06327,
        "t": 0.09356,
        "u": 0.02758,
        "v": 0.00978,
        "w": 0.02560,
        "x": 0.00150,
        "y": 0.01994,
        "z": 0.00077,
    }
  else:
    # Custom frequencies dictionary
    _UpperCamelCase           =    frequencies_dict

  if not case_sensitive:
    _UpperCamelCase           =    ciphertext.lower()

  # Chi squared statistic values
  _UpperCamelCase           =    {}

  # cycle through all of the shifts
  for shift in range(len(a__ ) ):
    _UpperCamelCase           =    ""

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        _UpperCamelCase           =    (alphabet_letters.index(letter.lower() ) - shift) % len(
            a__ )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    _UpperCamelCase           =    0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        _UpperCamelCase           =    letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.lower().count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    _UpperCamelCase           =    (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(a__ ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  _UpperCamelCase           =    min(
      a__    ,     key=a__    ,     )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      _UpperCamelCase
  )      ,						(
      _UpperCamelCase
  )      ,						
  )           =    chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )




